General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning

Raman spectroscopy is a powerful tool for identifying substances, yet accurately analyzing mixtures remains challenging due to overlapping spectra. This study aimed to develop a deep learning-based framework to improve the identification of components in mixtures using Raman spectroscopy. We propose a three-branch feature fusion network that leverages spectral pairwise comparison and a multi-head self-attention mechanism to capture both local and global spectral features. To address limited data availability, traditional data augmentation techniques were combined with deep convolutional generative adversarial networks (DCGAN) to expand the dataset. Our framework significantly outperformed existing Raman spectroscopy-based methods in both qualitative and quantitative analyses. The model demonstrated superior accuracy compared to U-Net and ResNext, achieving higher detection accuracy for mixture components. This framework offers a promising solution for improving mixture identification in Raman spectroscopy, with potential applications in industries such as pharmaceuticals, food safety, and environmental monitoring.